| ## RandAugment for Image Classification for Improved Robustness on the 🤗Hub! | |
| [Paper](https://arxiv.org/abs/1909.13719) | [Keras Tutorial](https://keras.io/examples/vision/randaugment/) | |
| Keras Tutorial Credit goes to : [Sayak Paul](https://twitter.com/RisingSayak) | |
| **Excerpt from the Tutorial:** | |
| Data augmentation is a very useful technique that can help to improve the translational invariance of convolutional neural networks (CNN). RandAugment is a stochastic vision data augmentation routine composed of strong augmentation transforms like color jitters, Gaussian blurs, saturations, etc. along with more traditional augmentation transforms such as random crops. | |
| Recently, it has been a key component of works like [Noisy Student Training](https://arxiv.org/abs/1911.04252) and [Unsupervised Data Augmentation for Consistency Training](https://arxiv.org/abs/1904.12848). It has been also central to the success of [EfficientNets](https://arxiv.org/abs/1905.11946). | |
| ## About The dataset | |
| The model was trained on [**CIFAR-10**](https://huggingface.co/datasets/cifar10), consisting of 60000 32x32 color images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images. | |
| --- | |
| language: | |
| - "List of ISO 639-1 code for your language" | |
| - en | |
| tags: | |
| - RandAugment | |
| - Image Classification | |
| license: apache-2.0 | |
| datasets: | |
| - cifar10 | |
| metrics: | |
| - Accuracy | |
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